A resource-allocating network for function interpolation
Neural Computation
Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems
Fuzzy Sets and Systems - Special issue on connectionist and hybrid connectionist systems for approximate reasoning
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
A General Framework for Symbol and Rule Extraction in Neural Networks
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2 - Volume 2
Intelligent control of the hierarchical agglomerative clustering process
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Subsethood-product fuzzy neural inference system (SuPFuNIS)
IEEE Transactions on Neural Networks
Midpoint-Validation Method for Support Vector Machine Classification
IEICE - Transactions on Information and Systems
Pipelined Genetic Algorithm Initialized RAN Based RBF Modulation Classifier
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Fuzzy neural network controller for AUV based on RAN
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Combining GAs and RBF neural networks for fuzzy rule extraction from numerical data
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
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In any neural network system, proper parameter initialization reduces training time and effort, and generally leads to compact modeling of the process under examination, i.e. less complex network structures and better generalization. However, in cases of multi-dimensional data, parameter initialization is both difficult and time consuming. In the proposed scheme a novel, multi-dimensional, unsupervised clustering method is used to properly initialize neural network architectures, focusing on resource allocating networks (RAN); both the hidden and output layer parameters are determined by the output of the clustering process, without the need for any user interference. The main contribution of this work is that the proposed approach leads to network structures that are compact, efficient and achieve best classification results, without the need for manual selection of suitable initial network parameters. The efficiency of the proposed method has been tested on several classes of publicly available data, such as iris, Wisconsin and ionosphere data.